Exceeds

MARCH 2026

iscas.ac.cn Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the iscas.ac.cn engineering team.

See how AI-active teams rank this week on the Exceeds Leaderboards.

The iscas.ac.cn engineering team reports 22.1% AI adoption, 0.38× productivity lift, and 12.7% code quality across recent work.

These metrics track how AI integrates into delivery pipelines, how throughput changes when assistance is used, and the health of AI-supported code review outcomes.

What this report measures

We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.

How to interpret these metrics

Use these signals to understand how AI assistance fits into day-to-day development, where enablement efforts drive throughput, and how review practices keep quality steady.

AI Adoption Rate

LOW

22.1%

AI assistance is present in 22.1% of recent commits for iscas.ac.cn.

AI Productivity Lift

LOW

0.38×

AI-enabled workflows deliver an estimated 0% lift in throughput.

AI Code Quality

LOW

12.7%

Review insights show 12.7% overall code health on AI-supported changes.

How is the iscas.ac.cn team performing with AI?

The iscas.ac.cn engineering team reports 22.1% AI adoption, translating into 0.38× productivity lift while sustaining 12.7% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.

Manager Questions Answered

Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.

What's a good company AI adoption rate?

iscas.ac.cn is at 22.1%. This is 21.6pp below the community median (43.7%)..

22.1%

Roughly in line43.7% Community Median

Spot squads sitting below the median and pair them with high-adoption champions to share workflows.

Does AI actually make developers faster?

iscas.ac.cn operates at 0.38×. This is 0.75× below the community median (1.13×)..

0.38×

↓0.75× below1.13× Community Median

Pilot AI-assisted grooming, ticket triage, or incident retros to create visible productivity wins.

How does AI affect code quality?

iscas.ac.cn holds AI-assisted quality at 12.7%. This is 10.5pp below the community median (23.2%)..

12.7%

↓10.5pp below23.2% Community Median

Add structured AI code review rubrics and require human sign-off for critical surfaces.

How evenly is AI use distributed across our team?

AI impact is concentrated—81.5% of AI commits come from a few experts, raising enablement risk.

81.5%

Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.

How can I prove AI ROI to executives?

To prove ROI, iscas.ac.cn needs steadier adoption, measurable lift, and consistent quality. The ingredients are forming but not yet executive-grade.

Start with a lighthouse project, measure cycle improvements end-to-end, and harden quality guardrails.

See how your full organization compares

Unlock personalized insights across all your repositories, teams, and contributors.

Securely connect Exceeds with your codebase to get commit-level insights on AI adoption and performance.

How Your Company Ranks

See how top engineering organizations compare across AI adoption, productivity lift, and code quality.

AI Adoption

% of commits with AI assistance

Companies in this quartile:

CA

cancun.tecnm.mx

(87.3%)

MO

momentohq.com

(87.3%)

UB

ub.edu

(21.2%)

RO

rossabaker.com

(21.2%)

Top 25% of teams adopt AI in 65-75% of their commits.

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

RO

rockstarwizard.ninja

(1.00×)

.I

.ieselrincon.es

(1.00×)

DR

draad.nl

(-9.59×)

WG

wgu.edu

(-0.41×)

Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.

Code Quality

Post-merge defect rate

Companies in this quartile:

GZ

gzgz.dev

(20.0%)

GW

gwu.edu

(20.0%)

DR

draad.nl

(-82634.9%)

IN

inria.fr

(-2424.6%)

Top 25% maintain quality above 92% while expanding AI usage, pairing automation with rigorous guardrails.

Rankings based on aggregated Exceeds AI dataset of 1.2M commits across open-source and enterprise engineering teams (Q4 2025).

Top contributors

Top contributors combine high AI adoption and quality output. Encourage internal sharing of best practices.

MZ

Meng Zhuo

Commits12
AI Usage94.5%
Productivity Lift1.43x
Code Quality92.6%
LH

Liutong HAN

Commits6
AI Usage24.0%
Productivity Lift1.06x
Code Quality20.0%
MY

MingZhu Yan

Commits4
AI Usage20.0%
Productivity Lift1.06x
Code Quality20.0%
JI

Jiawei

Commits26
AI Usage55.1%
Productivity Lift1.04x
Code Quality20.0%
SU

sunyuechi

Commits12
AI Usage39.7%
Productivity Lift1.02x
Code Quality20.0%

Encourage knowledge transfer from top AI users to others through internal mentoring or recorded "AI coding walkthroughs." Balanced adoption across the team typically improves overall performance by 12-15%.

Cross-Organization Network

Shared Repositories

20

XYenChi

rust-lang/gcc

Sakura286

yt-dlp/yt-dlp

sunyuechi

ossrs/ffmpeg-webrtc

google/XNNPACK

Liaoshihua

rust-lang/gcc

Unknown contributor

openssl/openssl

RuoqingHe

No repositories listed

Activity

149 Commits

Your Network

18 People
Sakura286
Member
XYenChi
Member
daichengrong@iscas.ac.cn
Member
RuoqingHe
Member
pz9115
Member
hanliutong
Member
ksco
Member
mengzhuo
Member
qjivy
Member

Why these metrics matter for engineering managers

Faster delivery

1.4x lift → predictable roadmaps

Safer velocity

93% quality → lower rollback risk

Equitable gains

AI less dependency on heroes

Governance

Depth monitoring audit-ready

ExceedsExceeds AI

Turns these insights into daily coaching and automatic alerts, helping managers balance speed with sustainability.

See the truth of AI impact

Adoption + lift + quality in one view

Learn more

Know where to act first

Repo and role level "lift potential"

Learn more

Prove ROI

Export executive snapshots and benchmarks

Learn more